MemoGen is a training-free agentic framework that stores task understanding, references, visual feedback, and lessons from past generations as reusable memory to improve text-to-image output over evolution rounds.
Genagent: Scaling text-to-image generation via agentic multimodal reasoning
8 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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Selective search plus generator-reasoner co-training improves knowledge-grounded image generation, but the reported gains are scored by the same VLM judge used to train the system.
COMFYCLAW introduces skill evolution via graph editing, automatic reversion, VLM verification, and distillation of runs into reusable Agent Skills, achieving higher average scores than a verifier-only baseline across benchmarks.
Qwen-Image-Agent is a unified agent framework that progressively builds sufficient generation context for T2I models via Context-Aware Planning and Context Grounding, achieving SOTA on IA-Bench, Mindbench, and WISE-Verified.
GenClaw introduces a three-stage code-driven workflow for agentic image generation that inserts programmatic sketches between linguistic reasoning and pixel synthesis.
NEWTON improves physical accuracy in video generation by deploying a trainable planner that coordinates physics-aware tools and a verifier, raising joint accuracy on VideoPhy-2 without altering the base generators.
GLANCE introduces a bi-loop multi-agent framework with global-local coordination mechanisms that outperforms baselines by up to 33% on music-grounded nonlinear video editing tasks using a new MVEBench benchmark.
GenEvolve introduces a self-evolving agent framework for image generation using tool-orchestrated trajectories and Visual Experience Distillation to achieve claimed SOTA results on benchmarks.
citing papers explorer
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MemoGen: Can Past Experience Improve Future Text-to-Image Generation?
MemoGen is a training-free agentic framework that stores task understanding, references, visual feedback, and lessons from past generations as reusable memory to improve text-to-image output over evolution rounds.
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Search Beyond What Can Be Taught: Evolving the Knowledge Boundary in Agentic Visual Generation
Selective search plus generator-reasoner co-training improves knowledge-grounded image generation, but the reported gains are scored by the same VLM judge used to train the system.
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COMFYCLAW: Self-Evolving Skill Harnesses for Image Generation Workflows
COMFYCLAW introduces skill evolution via graph editing, automatic reversion, VLM verification, and distillation of runs into reusable Agent Skills, achieving higher average scores than a verifier-only baseline across benchmarks.
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Qwen-Image-Agent: Bridging the Context Gap in Real-World Image Generation
Qwen-Image-Agent is a unified agent framework that progressively builds sufficient generation context for T2I models via Context-Aware Planning and Context Grounding, achieving SOTA on IA-Bench, Mindbench, and WISE-Verified.
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GenClaw: Code-Driven Agentic Image Generation
GenClaw introduces a three-stage code-driven workflow for agentic image generation that inserts programmatic sketches between linguistic reasoning and pixel synthesis.
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NEWTON: Agentic Planning for Physically Grounded Video Generation
NEWTON improves physical accuracy in video generation by deploying a trainable planner that coordinates physics-aware tools and a verifier, raising joint accuracy on VideoPhy-2 without altering the base generators.
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GLANCE: A Global-Local Coordination Multi-Agent Framework for Music-Grounded Non-Linear Video Editing
GLANCE introduces a bi-loop multi-agent framework with global-local coordination mechanisms that outperforms baselines by up to 33% on music-grounded nonlinear video editing tasks using a new MVEBench benchmark.
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GenEvolve: Self-Evolving Image Generation Agents via Tool-Orchestrated Visual Experience Distillation
GenEvolve introduces a self-evolving agent framework for image generation using tool-orchestrated trajectories and Visual Experience Distillation to achieve claimed SOTA results on benchmarks.